複合式類神經網路預測貨櫃船主機油耗

、線上人數：85

、訪客IP：3.145.33.244

姓名	傅國唐(Kuo-Tang Fu) 查詢紙本館藏	畢業系所	機械工程學系在職專班
論文名稱	複合式類神經網路預測貨櫃船主機油耗 (Prediction of Main Engine Fuel Consumption of Container Ships Based on Hybrid Artificial Neural Network)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 ( 永不開放)
摘要(中)	2023年起，國際航線船舶必須符合國際海事組織2023年EEXI（現有船舶能效指標）和CII（碳強度指標）要求，這些要求旨在大幅降低碳排放總量和碳排放強度。海事組織進一步制定戰略，要求船舶在2030年與2008年相比要減少40%的碳排放強度，到2050年更進一步要減少70%。因此，船舶運營商目前正在尋求可能的解決方案，以實現這一排放目標。在2030年之前，大多數船隻可通過優化船體設計和船舶速度調整（操作）方案進行碳排放強度的改善。而船速調整必須仰賴主機在各種船速與運行條件下的精準油耗預測結果，本研究是採用複合式類神經網路來預測主機油耗，與傳統上按照海試結果所繪製的船速油耗表相比，或是採用單一類神經網路所預測的油耗相比，採用複合式類神經網路可依照五種不同模型的預測結果設定適當配比，避免由於單一模型預測失準造成偏差。
摘要(英)	By 2023, vessels in international trade must comply with IMO (International Maritime Organization) EEXI (Energy Efficiency Index for Existing Ships) and CII (Carbon Intensity Index) requirements which are aiming at a significant reduction of both total carbon emission and carbon emission intensity. IMO has further set up a strategy that will request vessels to reduce carbon intensity by 40% in 2030 and total emission by 70% in 2050 compared to the 2008 baseline. Therefore, vessel operators are now seeking possible solutions that could achieve this emission goal. Before 2030, most vessels are improving optimized hull design and speed adjustment (operation). Speed adjustment should be based on the precise prediction of main engine fuel oil consumption under different speeds and engine working conditions. By applying the 5-model hybrid artificial neural network and an adequate proportion of each model in the mixture, one can reduce residuals in fuel oil consumption (FOC) prediction in comparison with the FOC-speed table obtained from sea trial or normal single model prediction.
關鍵字(中)	★ 氣候變遷 ★ 碳排放 ★ 複合式類神經網路	關鍵字(英)	★ Global Warming ★ Carbon Emission ★ hybrid artificial neural network
論文目次	摘要 iii Abstract iv 致謝 v 目錄 vi 圖目錄 ix 表目錄 xii 符號說明 xiv 第一章緒論 1 1-1 前言 1 1-1-1 聯合國氣候峰會(COP) 1 1-1-2 國際海事組織溫室氣體初始戰略 2 1-1-3 歐盟碳排放交易體系 6 1-2 研究動機與目的 7 1-3 研究流程 12 第二章文獻回顧 13 第三章類神經網路架構與原理 25 3-1 類神經網路介紹 25 3-1-1 類神經的概念 25 3-1-2 正向傳播 28 3-1-3 損失函數 29 3-2 反向傳播法 31 3-3 機器學習網路架構 37 3-3-1 感知器 38 3-3-2 多元線性迴歸 38 3-3-3 饋神經網路 40 3-3-4 支援向量機 40 3-3-5 徑向基函數 50 3-3-6 深度前饋神經網路 52 3-3-7 卷積神經網路 55 第四章以複合式類神經網路預測主機燃油消耗量之實作 57 4-1 資料蒐集 58 4-2 資料預處理 78 4-3 模型建構與訓練 92 4-3-1 模型挑選 92 4-3-2 標準化 94 4-3-3 k折驗證 97 4-3-4 超參數選定 98 4-3-5 模型訓練 101 4-3-6 模型評估 102 4-3-7 模型調配 102 4-4 模型訓練結果 106 4-4-1 SVM模型實驗結果： 106 4-4-2 LASSO模型實驗結果： 108 4-4-3 Ridge模型實驗結果： 110 4-4-4 ElasticNet模型實驗結果： 112 4-4-5 Stacking模型實驗結果： 114 4-5 模型混合結果 116 第五章結論與未來展望 121 5-1 結論 121 5-2 未來展望 122 參考文獻 123 附錄一實際執行步驟與程式說明 125
參考文獻	[1] IMO. IMO Train the Trainer (TTT) Course on Energy Efficient Ship Operation - Module 2: Ship Energy Efficiency Regulations and Related Guidelines. 2014. [2] (NEa), T.D.E.A. Infographics: How does the EU ETS work? 2015. [3] DNV-GL. Energy-Transition-Outlook 2019. 2019. [4] Bouman, E.A., et al., State-of-the-art technologies, measures, and potential for reducing GHG emissions from shipping–A review. Transportation research part D: Transport and environment, 2017. 52: p. 408-421. [5] Schrady, D.A., G.K. Smyth, and R.B. Vassian, Predicting Ship Fuel Consumption: Update. 1996, NAVAL POSTGRADUATE SCHOOL MONTEREY CA DEPT OF OPERATIONS RESEARCH. [6] Holtrop, J. and G. Mennen, An approximate power prediction method. International Shipbuilding Progress, 1982. 29(335): p. 166-170. [7] TRYON, J., Cube 3: a model for calculating fuel consumption for gas turbine ships. Center for Naval Analyses Research Memorandum CRM, 1986: p. 86-213. [8] Erto, P., et al., A procedure for predicting and controlling the ship fuel consumption: its implementation and test. Quality and Reliability Engineering International, 2015. 31(7): p. 1177-1184. [9] Tsitsilonis, K.-M. and G. Theotokatos, A novel systematic methodology for ship propulsion engines energy management. Journal of cleaner production, 2018. 204: p. 212-236. [10] Kwon, Y., Speed loss due to added resistance in wind and waves. Nav Archit, 2008. 3: p. 14-16. [11] Lu, R., O. Turan, and E. Boulougouris. Voyage optimisation: prediction of ship specific fuel consumption for energy efficient shipping. in Low Carbon Shipping Conference, London. 2013. [12] Sanchez, P.J. Fundamentals of simulation modeling. in 2007 Winter Simulation Conference. 2007. IEEE. [13] Blochwitz, T., et al. The functional mockup interface for tool independent exchange of simulation models. in Proceedings of the 8th international Modelica conference. 2011. Linköping University Press. [14] Guan, C., G. Theotokatos, and H. Chen, Analysis of two stroke marine diesel engine operation including turbocharger cut-out by using a zero-dimensional model. Energies, 2015. 8(6): p. 5738-5764. [15] Zhao, F., et al., An overall ship propulsion model for fuel efficiency study. Energy Procedia, 2015. 75: p. 813-818. [16] Beşikçi, E.B., et al., An artificial neural network based decision support system for energy efficient ship operations. Computers & Operations Research, 2016. 66: p. 393-401. [17] Gkerekos, C., I. Lazakis, and S. Papageorgiou, Leveraging big data for fuel oil consumption modelling. 2018. [18] McCulloch, W.S. and W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 1943. 5(4): p. 115-133. [19] Basheer, I.A. and M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application. Journal of microbiological methods, 2000. 43(1): p. 3-31. [20] Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. nature, 1986. 323(6088): p. 533-536. [21] Moreira, L., R. Vettor, and C. Guedes Soares, Neural network approach for predicting ship speed and fuel consumption. Journal of Marine Science and Engineering, 2021. 9(2): p. 119. [22] Pedregosa, F., et al., Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 2011. 12: p. 2825-2830. [23] Cortes, C. and V. Vapnik, Support-vector networks. Machine learning, 1995. 20(3): p. 273-297. [24] Gkerekos, C., I. Lazakis, and G. Theotokatos, Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study. Ocean Engineering, 2019. 188: p. 106282. [25] Hinton, G.E. and R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. science, 2006. 313(5786): p. 504-507. [26] Krizhevsky, A., I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25. [27] ISO. ISO 3046-1 5th Edition. 2002. [28] Meiyo Electric Co., L., Platinum Thermal Resistance Bulb & Thermo-Couple Product Guide. 2012. [29] Breiman, L., Stacked regressions. Machine learning, 1996. 24(1): p. 49-64. [30] Hu, Z., et al., A Novel Hybrid Fuel Consumption Prediction Model for Ocean-Going Container Ships Based on Sensor Data. Journal of Marine Science and Engineering, 2021. 9(4): p. 449.
指導教授	李天錫傅尹坤(Tien-Hsi Lee Yiin-Kuen Fuh)	審核日期	2022-7-11
推文	facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu
網路書籤	Google bookmarks del.icio.us hemidemi myshare

博碩士論文 109353004 詳細資訊